During the last two posts i presented the reasons and some problems on analyzing the thoughts of users on the web and particularly Twitter. (For more see Part1 and Part2 ).
The next step is to remove all phrases that do not give us any information about what users do not want :
Finally we remove the phrase “i don’t want to”. However, consider the following example:
“I must go to Chicago. I don’t want to do that”
The steps discussed above will discard the first sentence which is actually what the user does not want to do and leave only the phrase “i don’t want to do that” which is not particularly informative. At this point we must quantify the problem -let’s assume it involves the 8.5% of our records- and recall what the pareto principle is all about.
After some additional pre-processing steps which are not discussed here, i feed the data to K-Means to see the clusters the algorithm comes up with. For a better pres…
During the last two posts i presented the reasons and some problems on analyzing the thoughts of users on the web and particularly Twitter. (For more see Part1 and Part2 ).
The next step is to remove all phrases that do not give us any information about what users do not want :
Finally we remove the phrase “i don’t want to”. However, consider the following example:
“I must go to Chicago. I don’t want to do that”
The steps discussed above will discard the first sentence which is actually what the user does not want to do and leave only the phrase “i don’t want to do that” which is not particularly informative. At this point we must quantify the problem -let’s assume it involves the 8.5% of our records- and recall what the pareto principle is all about.
After some additional pre-processing steps which are not discussed here, i feed the data to K-Means to see the clusters the algorithm comes up with. For a better presentation of the results, here is a screen capture from IBM’s UI Modeler :
We immediately see -in descending order- what Tweeter users do not want :
1) They don’t want to go to work
2) They don’t want to go to school
3) They don’t want to hear about various issues
4) They don’t want to stay home
Notice also the top two categories named Miscellaneous and None. These categories contain thoughts that have a very small frequency to form a cluster. These two categories consist the 69.56% of our records and at this point we should think again about the pareto principle.
Please note that not all necessary work is discussed here and i had to omit several actions that have to take place. In trying to understand what people actually think i am using an approach which uses Ontologies, Information Extraction, Clustering and Classification analysis with the ultimate goal to minimize the percentage of thoughts (69.56% in this example) that cannot form a cluster and to increase the accuracy of the analysis.
It is also an interesting fact that we could move further down the sentence branch (see this post) for even better insight. Here i presented a clustering analysis about what users do not want. As an example we could apply clustering on user thoughts for “I don’t want to feel”.